Poster

WorldTravel: A Realistic Multimodal Travel-Planning Benchmark with Tightly Coupled Constraints

Zexuan Wang ⋅ Chenghao Yang ⋅ Yingqi Que ⋅ Zhoufutu Wen ⋅ Zaiyuan Wang ⋅ Jiashuo Liu ⋅ Zhixin Yao ⋅ Zhenzhu Yang ⋅ Huaqing Yuan ⋅ Yiwen Wang ⋅ Zhengxuan Jiang ⋅ Shengjie Fang ⋅ Zhenhe Wu ⋅ Zhaohui Wang ⋅ JinCheng Ren ⋅ Yuzhen Li ⋅ YangYang ⋅ Jiaheng Liu ⋅ Jian Yang ⋅ Ge Zhang ⋅ Wenhao Huang

Abstract

Real-world autonomous planning requires coordinating tightly coupled constraints where a single decision dictates the feasibility of all subsequent actions. However, existing benchmarks predominantly feature loosely coupled constraints solvable through local greedy decisions and rely on idealized data, failing to capture the complexity of extracting parameters from dynamic web environments. We introduce $\textbf{WorldTravel}$, a benchmark comprising 150 real-world travel scenarios across 5 cities that demand navigating an average of 15+ interdependent temporal and logical constraints. To evaluate agents in realistic deployments, we develop $\textbf{WorldTravel-Webscape}$, a multi-modal environment featuring over 2,000 rendered webpages where agents must perceive constraint parameters directly from visual layouts to inform their planning. Our evaluation of 10 frontier models reveals a significant performance collapse: even the state-of-the-art GPT-5.2 achieves only 28.0\% feasibility in text-only settings, which plummets to 3.4\% in multi-modal environments. We identify a critical Perception-Action Gap and a Planning Horizon threshold at approximately 10 constraints where model reasoning consistently fails, suggesting that perception and reasoning remain independent bottlenecks. These findings underscore the need for next-generation agents that unify high-fidelity visual perception with long-horizon reasoning to handle brittle real-world logistics.